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16 March 2016 Trophic Ecology of a Predatory Community in a Shallow-Water, High-Salinity Estuary Assessed by Stable Isotope Analysis
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Abstract

Estuaries serve as habitats and nurseries for many recreationally and commercially important fishes, often contributing recruits to adult populations that remain in close proximity to estuarine environments. Upper-level predatory fish species are among the most sought after by fisheries; thus, an understanding of the trophic dynamics of the community can assist ecological fisheries management of these highly productive ecosystems. Dietary niche overlap within the predatory fish community of Bulls Bay, a subtropical estuary in South Carolina, was assessed by using stable isotope analyses (δ13C and δ15N) to compare seven elasmobranch species and three teleost species. Cownose Rays Rhinoptera bonasus and Finetooth Sharks Carcharhinus isodon had no isotopic overlap with other species and therefore exhibited unique isotopic niche spaces that were indicative of potential resource partitioning. The teleosts and remaining elasmobranchs had varying degrees of overlap, implying shared resources; a high degree of dietary niche overlap was observed among Spotted Seatrout Cynoscion nebulosus, Sandbar Sharks Carcharhinus plumbeus, and Atlantic Sharpnose Sharks Rhizoprionodon terraenovae. Although most pairs of species showed some isotopic overlap, there were also interspecific differences in niche overlap, signifying that this predatory fish community has a widely varied prey base overall. Bulls Bay is an important nursery habitat with a balanced predator community, as illustrated by a combination of unique dietary niches and varying degrees of dietary niche overlap.

South Carolina estuaries are critical habitats for many economically and ecologically important fish species (Wenner et al. 1990; Castro 1993; Blaber et al. 2000; Ulrich et al. 2007), which comprise a predator community similar to those found in other estuarine waters along the southeastern U. S. and Gulf of Mexico coasts (Moncreiff and Sullivan 2001; Bethea et al. 2004; Lellis-Dibble et al. 2008). Juveniles of teleost species with recreational angling importance utilize shallow tidal creeks and stretches of marsh vegetation in South Carolina estuaries as primary habitat (Wenner et al. 1990). Estuaries in South Carolina serve as communal nurseries for several coastal shark species (Castro 1993; Ulrich et al. 2007), some of which are also important targets of recreational fisheries. The presence of many species at an important stage of growth within a common habitat raises questions about the availability and abundance of resources within estuaries and about whether shared resources may be a cause for competition among co-existing teleosts and elasmobranchs.

Predatory fishes link pelagic and benthic food chains in aquatic systems through trophic interactions (Rooney and McCann 2012; Baustian et al. 2014) and may therefore influence and have a stabilizing effect on the trophic structure of a community or ecosystem (Arim et al. 2010; Rooney and McCann 2012). Little is known about trophic interactions between elasmobranchs and teleosts—which together represent a mid- to upper-level predator community—in South Carolina estuaries (Cortes 1999; Akin and Winemiller 2008). One way to determine a community's connectivity is by studying foraging ecology and dietary overlap (Herzka 2005), which can be used to infer trophic ecology, interspecific competition, and niche partitioning within a community (Newsome et al. 2007).

The traditional method of studying the diets of one or more species is stomach content analysis (SCA). However, SCA for dietary overlap studies often requires the sacrifice of a large number of fish to properly characterize the diets, as was illustrated in previous diet studies conducted using cumulative prey curves (Ferry and Cailliet 1996; Cortes 1997; Hoffmayer and Parsons 2003; Bethea et al. 2004; Torres-Rojas et al. 2010). In addition, SCA results represent only a snapshot of the diet and may not adequately describe the diet in its entirety due to differential digestion of prey and differential food availability (Hyslop 1980; Pinnegar and Polunin 1999).

Stable isotope analysis (SIA) can be a minimally invasive, nonlethal technique (Baker et al. 2004) that provides information on the assimilated diet of an organism, reflecting its previous trophic history. Carbon and nitrogen isotope ratios (δ13C and δ15N, respectively) are time-integrated measures of diet that reflect long-term foraging by an individual (tissue turnover ranging from ∼40 d for juvenile teleosts to ∼200–500 d for elasmobranchs and slower-growing teleosts; MacAvoy et al. 2001; Suzuki et al. 2005; MacNeil et al. 2006; Logan and Lutcavage 2010) and the assimilated organic material of a diet rather than a snapshot of the organism's recent prey as determined from traditional SCA (Peterson and Fry 1987; Gannes et al. 1997). The SIA method bypasses the problem of empty stomachs due to regurgitation, the bias of opportunistic feeding, and the confounding effects of differential digestibility of prey items in the stomach (Borrell et al. 2011). The SIA approach is based on the predictive enrichment of δ13C and δ15N between a basal source or prey item and a consumer. Carbon isotopic ratios exhibit little enrichment between trophic levels; they primarily reflect basal carbon sources from the benthic or pelagic source upon which an organism is feeding if there are at least two isotopically distinct carbon sources within the ecosystem (Peterson and Fry 1987), thereby providing information about carbon flow in the estuary. Stable nitrogen fractionates to a greater extent between trophic levels and is therefore indicative of an individual's relative trophic position within the ecosystem (DeNiro and Epstein 1981; Minagawa and Wada 1984; McCutchan et al. 2003). In a review of isotopic fractionation values for teleost muscle, Caut et al. (2009) found an average diet-tissue discrimination value of 2.5‰ for nitrogen isotopes and 1.8‰ for carbon isotopes. Elasmobranchs have various fractionation values that, according to recent research, are species specific. For example, Hussey et al. (2010) found a factor of 2.4‰ for nitrogen isotopes and a factor of 0.9‰ for carbon isotopes in the muscle of Sand Tigers Carcharias taurus. Kim et al. (2012) reported a fractionation of 3.7‰ for nitrogen isotopes and 1.7‰ for carbon isotopes in the muscle of Leopard Sharks Triakis semifasciata.

Fishing pressure and habitat loss threaten the health of estuarine ecosystems and can lead to imbalances in estuarine community structure (Pinnegar et al. 2000; Kennish 2002). Therefore, baseline data are needed to track community changes over time. Studies of competition and dietary niche overlap among shark species are often complicated by these species' generally large sizes, relatively low abundances, and high mobility. However, isotopic data are relatively easy to collect from a large number of individuals and species, particularly within certain estuaries. In the southeastern USA, some shallow-water estuaries, such as Bulls Bay, South Carolina, act as nursery areas for coastal sharks and therefore contain relatively high numbers (i.e., relative to other coastal habitats) of juvenile sharks representing several species, thereby allowing dietary studies to be conducted with relative ease (Papastamatiou et al. 2006). The use of SIA is now common in ecological studies analyzing resource use by fishes, examining food web structure, defining relative trophic positions, and measuring dietary niche breadth of individual fish species and dietary niche overlap between fish species in communities (Layman et al. 2007; Hussey et al. 2012; Shiffman et al. 2012).

Many current and past fisheries management strategies have focused on single-species stock assessment models and have often overlooked the importance of predation and trophic interactions (Latour et al. 2003). Ecosystem-based fishery management is a holistic approach that considers the diets and trophic relationships of several species and prioritizes the ecosystem—rather than a single species—as its focus (Pikitch et al. 2004; Levin et al. 2009; Link and Browman 2014). Mid- to upper-level predatory fishes are among the species that are most sought after by commercial and recreational fishers in estuarine systems. Changes in estuarine trophic structure due to fishing pressure heighten the importance of determining the trophic ecology and dietary niches of predatory species to infer interspecific relationships within estuarine fish communities. These trophic data can contribute valuable information for the development of ecosystem-based fisheries management plans.

We examined the trophic ecology of an estuarine-dependent predatory fish community in Bulls Bay (Figure 1) by using SIA to analyze the dietary niche overlap of 10 co-occurring fish species (seven elasmobranchs and three teleosts) and to infer their potential prey. In addition to elucidating important trophic information about each predator species, this study presents trophic connections within the community and explores the possibility of resource sharing, partitioning, or both among the predators.

FIGURE 1.

Map of sampling locations in Bulls Bay, South Carolina, and surrounding waters. Samples were collected via gill nets and longlines through the Cooperative Atlantic States Shark Pupping and Nursery (COASTSPAN) survey and via trammel nets through the South Carolina Department of Natural Resources estuarine finfish survey.

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METHODS

Study site and sample collection.—This study took place in the Bulls Bay estuarine system, located within the Cape Romain Wildlife Refuge, South Carolina. The estuary has minimal freshwater discharge and is considered a highsalinity, non-salt-wedge estuary (Mathews et al. 1980; Sandifer et al. 1980). It consists of anastomosing small creeks and deeper central channels with shallow mudflats along raised vegetated edges comprised mostly of saltmarsh cordgrass Spartina alterniflora.

To perform SIA, we collected muscle tissue samples from seven elasmobranchs (six shark species and one ray species): the Atlantic Sharpnose Shark Rhizoprionodon terraenovae, Blacktip Shark Carcharhinus limbatus, Bonnethead Sphyrna tiburo, Finetooth Shark Carcharhinus isodon, Sandbar Shark Carcharhinus plumbeus, Scalloped Hammerhead Sphyrna lewini, and Cownose Ray Rhinoptera bonasus. We also collected samples from three teleost species: Red Drum Sciaenops ocellatus, Southern Flounder Paralichthys lethostigma, and Spotted Seatrout Cynoscion nebulosus. The majority of elasmobranchs sampled were considered young of the year (age 0) or juveniles, with the exception of Bonnetheads and some Atlantic Sharpnose Sharks. To limit maternal influence on the isotopic signatures of elasmobranchs, muscle samples were taken only from sharks with fully healed umbilical scars (Olin et al. 2011). All of the sampled Red Drum and most of the Southern Flounder were considered juveniles, whereas all Spotted Seatrout and some Southern Flounder were of adult size according to the maturity indices of Wenner et al. (1990). Cownose Rays have a specialized diet consisting of bivalve mollusks (Collins et al. 2007; Ajemian and Powers 2011) and were included as an outgroup or reference species to the other predatory fishes in various analyses because their diet differs from the more varied diets of sharks and teleosts, which feed on prey from higher trophic levels. Potential prey taxa were selected from past SCAs conducted on the 10 predator species we analyzed (Wenner et al. 1990; Bush 2003; Bethea et al. 2004; Ellis and Musick 2007); in total, 24 prey species that had been identified as important in the diets of one or more predator species were sampled in Bulls Bay.

Because the species of interest show seasonally changing abundance patterns, samples were only collected between May and early October in 2011 and 2012. Three gear types were used for collecting samples at locations in Bulls Bay: longlines, gill nets, and trammel nets. Elasmobranchs were primarily caught by using a hand-deployed longline and gill nets at fixed stations within Bulls Bay, whereas teleost samples were typically collected by trammel nets that were set adjacent to Bulls Bay (Figure 1).

Teleost fishes were measured in SL (mm), sharks were measured in FL, and rays were measured in disk width (DW) before being released. Sex was determined for all elasmobranchs; however, sex determination for teleosts required gonad analysis, which only occurred for individuals that were sacrificed for use in other studies. Muscle samples (∼60 mg) for SIA were taken from the dorsal musculature with a sterile, 4-mm, disposable biopsy punch (Premier Medical Products Unipunch) and were placed in 2-mL cryovial tubes; upon arrival at the laboratory, the samples were frozen at -80°C until processing.

We collected live saltmarsh cordgrass, live phytoplankton, and surface sediment samples from areas adjacent to predator sampling sites so as to characterize the basal carbon sources of estuarine primary producers. Phytoplankton samples were collected by surface plankton tows, and zooplankton was separated from phytoplankton by viewing the samples under magnification. Sediment samples were collected from the top 2 mm of marsh mud surface that was covered with water; the samples represented a mixture of basal resources available on the sediment (Wright et al. 2014). Mud samples were homogenized and subsampled for use in SIA. The sediment samples were not treated for carbonate reduction, but 17 of the samples were tested to average out the benthic algae signature. In addition, our sediment isotopic signatures were comparable to those observed by Wright et al. (2014) in a Georgia estuary that is very similar to Bulls Bay (dominated by saltmarsh cordgrass, mudflats, and oyster reefs). All materials collected for SIA were frozen at -80°C until processing.

Stable isotope processing.—Samples were thawed, and skin was removed when present. Muscle samples were lyophilized (VirTis Genesis XL25, Wizard 2.0; SP Scientific) for at least 40 h, and the tissue was ground into a fine powder with a bead beater (Mini-Bead Beater 8, BioSpec Products). To reduce sources of error and to standardize the data across multiple species (Hussey et al. 2012), we extracted the lipids by following a modified Bligh and Dyer (1959) method in which approximately 2 mL of 2:1 chloroform: methanol solution were added to each powdered sample. The samples were placed in a warmwater bath, sonicated for 15 min (Fisher-Scientific FS50), and centrifuged (IEC Centra CL3) at 1,200 rotations/min for 5 min; the solvent was extracted from each tube. The process was then repeated. In addition to lipid removal, this method is designed to remove urea from elasmobranch muscle tissue (Christie 1993), which is important because urea in the muscle may misrepresent the isotopic values by exhibiting more depleted δ13C values relative to those observed when urea is removed (Kim and Koch 2012).

Stable isotope analysis was performed by using a Thermo Flash Elemental Analyzer coupled to a ThermoFisher Scientific Delta V Plus isotope ratio mass spectrometer at the Skidaway Institute of Oceanography's (SKIO) Scientific Stable Isotope Laboratory (SSIL), Savannah, Georgia. Stable isotopes were expressed in delta notation as differences (‰) from a standard,

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where Rsample refers to the ratio of heavy isotope to light isotope (13C/12C and 15N/14N); and Rstandard is the Pee Dee Belemnite standard for carbon or the atmospheric N2 standard for nitrogen. Throughout the analysis, chitin powder standards were run as a control for isotopic values (δ13C: mean ± SD = -19.0 ± 0.09‰; δ15N: -1.0 ± 0.09‰; from 2012 samples). The long-term SD (a measure of precision) at SKIO-SSIL is ±0.2‰ for both δ13C and δ15N (J. Brandes, SKIO-SSIL, personal communication).

Statistical analysis.—The assumption that data were normally distributed was tested by using a Shapiro-Wilk test for multivariate data. Differences in mean bivariated isotope ratios (δ13C and δ15N) across all predators were assessed by multivariate ANOVA (MANOVA), which was followed by a univariate ANOVA to evaluate whether differences in isotopic niche space were due to differences in both δ13C and δ15N. We used post hoc Tukey's honestly significant difference (HSD) tests to group species that exhibited significantly different mean δ13C and δ15N values.

Quantitative metrics to assess interspecific comparisons were calculated according to Layman et al. (2007). Metrics (hereafter, “Layman metrics”) included the δ15N and δ13C range between the most- and least-enriched species; the mean distance to the centroid (CD); the mean nearest-neighbor distance (M-NND); the SD of nearest-neighbor distance (SD-NND); and the mean distance of each individual to all other individuals. The CD represents the average degree of trophic diversity within a species, whereas the M-NND and SD-NND represent the density and evenness, respectively, of species packing within the isotopic niche space.

The standard ellipse area (SEA) for each species was calculated as an estimate of isotopic niche width; we used a corrected SEA (SEAC), which reduces bias for small sample sizes (Jackson et al. 2011; Syvaranta et al. 2013). Dietary niche overlap was calculated with Stable Isotope Bayesian Ellipses for R (SIBER) using the Stable Isotope Analysis in R (SIAR) package (Parnell and Jackson 2013). To gain insight into food web dynamics, we used the SIAR package's Bayesian mixing models in an attempt to estimate the contribution of carbon source as well as potential prey items for each species. Fractionation values (correction factors in SIAR) between resources and consumers with lipid-extracted samples were assumed to be 1.8 ± 0.65‰ (mean ± SD) for δ13C and 3.2 ± 0.74‰ for δ15N based on a meta-analysis by McCutchan et al. (2003). Because there are multiple trophic levels between the basal resources and the predators, these fractionation values were multiplied by 3 (Phillips 2012). Up to 500,000 iterations were used for each species group.

To determine whether body size was a factor in δ15N or δ13C differences between species, linear regressions or nonparametric tests (Kendall's robust line fit method) were used to detect relationships between δ13C or δ15N values and size for each predator species. Relationships between isotopic values and capture day were explored to detect possible changes in the diet throughout the summer or to detect maternal influence on isotopic signatures of age-0 elasmobranchs. Placentatrophic neonate sharks have an enriched signature relative to their mothers (Olin et al. 2011). The individuals with isotopic signatures that most likely reflected the maternal signatures (i.e., the earliest-caught age-0 Atlantic Sharpnose Sharks) were removed from analyses. All statistical analyses were performed in R software (R Development Core Team 2010).

RESULTS

Overall, 313 muscle samples for use in SIA were collected from 10 predatory fish species (Table 1). Average δ13C values of the 10 species ranged from -18.16‰ to -15.82‰ (Table 2). Average δ15N values spanned a larger range than δ13C (from 9.42‰ to 15.22‰), indicating that the predators fed on prey across multiple trophic levels. The variance of average isotopic values for individual species was diverse for both δ13C and δ15N, suggesting the presence of varied feeding strategies in the predator community. Among the apex predators, Scalloped Hammerheads were the most enriched in δ15N (mean ± SD = 15.22 ± 0.85‰), whereas Red Drum were the least enriched in δ15N (11.88 ± 0.82‰). Feeding at a lower trophic level than the other predators, Cownose Rays displayed the lowest mean δ15N value (9.42 ± 0.52‰). Red Drum were the most enriched in δ13C (-15.82 ± 0.78‰), whereas Finetooth Sharks were the least enriched (-18.16 ± 0.27‰). According to Layman metric values, Finetooth Sharks and Blacktip Sharks had the lowest amount of isotopic variation (Table 2). Among the predators we examined, Finetooth Sharks had the smallest ranges of δ13C and δ15N and the smallest CD, M-NND, and SD-NND values; Blacktip Sharks had the next-smallest values for all metrics except M-NND. Overall, across the calculated Layman metrics, the teleost species had more average variation than the elasmobranchs (Table 2).

TABLE 1.

Capture dates of predator species in Bulls Bay, South Carolina, expressing the seasonal range of captures (encompasses data from both 2011 and 2012). Sizes are reported in FL for sharks, disk width (DW) for Cownose Rays, and SL for teleosts.

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Relationships between isotopic values and predator size as well as capture day (day of the year) were assessed by use of regression models (Table 3). Species with a significant relationship between only one isotopic ratio and either size or capture day were considered to constitute a single group. Red Drum displayed a linear relationship between size and both of the isotopic ratios. There was a significant difference in δ13C and δ15N between length-groups (<400 and ≥400 mm SL) of Red Drum (t-test, δ13C: t = -2.51, P = 0.02; δ15N: t = -2.81, P = 0.007); however, there was a gap in the data due to a lack of intermediate lengths for Red Drum, and the large amount of variation in the regressions between isotope ratios and fish size resulted in low R2 values (δ13C: R2 = 0.07; δ15N: R2 = 0.15). Therefore, Red Drum remained as a single group, although we analyzed the size-classes separately to investigate ontogenetic shifts. When analyzed as different length-groups (244–377 and 400–642 mm SL), larger Red Drum were found to have a higher δ15N signature than smaller fish.

Data for eight of the predator species were normally distributed, but the δ13C and δ15N distributions for Atlantic Sharpnose Sharks and Cownose Rays were nonnormal even after multiple data transformations. For the nonnormal data, we used Kendall's robust model (a nonparametric test) to assess the relationship between isotopic values and size or capture day (Table 4). When isotopic data for Atlantic Sharpnose Sharks were divided according to the month of capture, data were normal for age-0 sharks caught during August, whereas data for July-caught age-0 individuals were nonnormal and therefore were removed from analyses. The nonnormal distribution of isotopic signatures could be attributable to the presence of very different size-groups within the sample (i.e., shark pups and adults). In addition, the July-caught age-0 Atlantic Sharpnose Sharks had significant relationships between capture day and both δ13C and δ15N, suggesting a possible maternal input that reflected the foraging location used by the mothers; therefore, these age-0 signatures were likely enriched in δ13C and δ15N relative to the maternal tissues (McMeans et al. 2009; Vaudo et al. 2010; Olin et al. 2011). The assumption of normality was relaxed in analyses for Cownose Rays since they served as the out-group.

TABLE 2.

Layman metrics (Layman et al. 2007) and standard ellipse areas (SEAs) for members of the predator community in Bulls Bay, South Carolina. The second row for Atlantic Sharpnose Sharks refers to the Layman metric and SEA analyses conducted only with data from August-caught age-0 sharks (normally distributed isotopic values), which are referred to here simply as age 0; mean (±SD) and range of δ15N and δ13C are also presented (CD = mean distance to the centroid; M-NND = mean nearest-neighbor distance; SD-NND = SD of nearest-neighbor distance; SEAc = corrected SEA).

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TABLE 3.

Results of linear regression testing the effects of fish size (FL for sharks, SL for teleosts, or disc width [DW] for rays) and capture day (i.e., day of the year; data were combined across years to depict temporal changes) on δ13C and δ15N in the muscle tissue of predators sampled in Bulls Bay estuary.

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TABLE 4.

Results of nonparametric Kendall's robust models of Atlantic Sharpnose Sharks and Cownose Rays (for which data were nonnormally distributed), testing the effects of size (FL for sharks; disc width [DW] for rays) and capture day (day of the year; data were combined across years to depict temporal changes) on δ13C and δ15N in muscle tissue. Data for Atlantic Sharpnose Sharks represent analyses that were conducted on August-caught age-0 individuals and adults.

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There was a significant difference in δ13C between the 2011 and 2012 sampling years for all species except the Cownose Ray, which had a δ13C value (mean ± SD) of -17.23 ± 0.95‰ in 2011 and a value of -16.22 ± 0.70‰ in 2012. There were no significant differences in δ15N between study years for any of the species. Data from both years were combined for all species because (1) a minimum sample size of 30 individuals for isotopic analysis was recommended by Syvaranta et al. (2013), particularly when Bayesian ellipses and mixing models are used; and (2) more robust results were desired.

FIGURE 2.

Mean (±SD) isotopic signatures (δ13C and δ15N) for elasmobranchs and teleosts belonging to the predator community in Bulls Bay, South Carolina. Only data from age-0 Atlantic Sharpnose Sharks that were caught during August are included in the mean values for that species.

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The average isotopic values of predators had significantly different locations in isotopic niche space (MANOVA: F = 73.1, P < 0.001; Figure 2) due to significant differences in δ13C and δ15N among all species (ANOVA, δ13C: F = 32.6, P < 0.001; δ15N: F = 268, P < 0.001). The post hoc Tukey's HSD test separated the 10 predators into four groups with similar δ13C values and into seven groups with similar δ15N values (Table 5). Based on δ13C means, groups 2 and 3 demonstrated considerable overlap and were significantly different from group 1 (Red Drum) and group 4 (Finetooth Shark and Cownose Ray). For the δ15N means, the only overlap identified by Tukey's HSD test was between group 3 (Sandbar Shark and Atlantic Sharpnose Shark) and group 4 (Spotted Seatrout and Atlantic Sharpnose Shark).

TABLE 5.

Results of post hoc Tukey's honestly significant difference tests, with elasmobranch and teleost species grouped by mean δ13C values (indicating the range of carbon sources utilized by the predators) and mean δ15N values (expressing the relative trophic levels of the predators). Asterisks indicate the group (Gr) to which a species belongs.

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More extensive dietary overlap among species was detected via SIBER analysis when we included all isotopic signatures rather than simply the mean for each species in niche space (Table 6; Figure 3). Finetooth Sharks and Cownose Rays had no dietary niche overlap with any other species. In one instance, the standard ellipses of three species (Spotted Seatrout, Sandbar Shark, and Atlantic Sharpnose Shark) significantly overlapped with each other. Bonnetheads showed the highest overlap, as 98.3% of their standard ellipse overlapped with that of Southern Flounder (Table 6). Overall, dietary niche overlap was slightly less among teleosts than among sharks; the highest area of overlap was observed between Red Drum (33.6% of total isotopic niche area) and Southern Flounder (25.4% of total isotopic niche area). Red Drum and Spotted Seatrout exhibited no dietary niche overlap. In comparison, 41.0% and 20.9% of the ellipse for Blacktip Sharks overlapped with the Scalloped Hammerhead ellipse and the Sandbar Shark ellipse, respectively. Overlap in the ellipses of Atlantic Sharpnose Sharks and Sandbar Sharks constituted 42.7% of the Atlantic Sharpnose Shark ellipse and 29.5% of the Sandbar Shark ellipse.

There were two main carbon resources upon which the predator community relied: phytoplankton and benthic carbon (Appendix Table A.1). Finetooth Sharks and Cownose Rays had isotopic signatures very similar to those of phytoplankton, suggesting that those predators relied heavily on the planktonic food web. Some of the Red Drum had δ13C values similar to those of saltmarsh cordgrass; however, this carbon source was probably not as important to the diets of the other fish species. The majority of predator isotopic means were most similar to sediment or benthic carbon isotopic values. We collected a total of 116 muscle samples across 24 potential prey species (Table A.1). In general, the isotopic signatures of the potential prey items (6 invertebrate species and 18 teleost species) were intermediate between the δ15N values of the secondary consumers and the basal resources (i.e., they occupied the isotopic niche space that would be expected for primary consumers in a food web).

DISCUSSION

An understanding of food web structure and resource use in an ecologically and economically important ecosystem is necessary to comprehend the trophic structure of communities. In addition, knowledge of a community or ecosystem's trophic ecology is necessary for detecting changes in trophic structure that can occur due to natural effects (e.g., population shifts or localized species die-offs due to temperature intolerance) or anthropogenic effects (e.g., fishing or development). Use of SIA allows researchers to gain trophic information on a complicated estuarine food web—information that would otherwise be taxing to obtain via traditional diet study methods, such as SCA (which requires sacrificing the animal) or gastric lavage.

TABLE 6.

Percentage overlap in standard ellipses (determined using the corrected standard ellipse area) between each pair of predator species. The table is to be read across each row; for example, 35.7% of the Atlantic Sharpnose Shark ellipse overlaps with the Sandbar Shark ellipse, and 21.9% of the Sandbar Shark ellipse overlaps with the Atlantic Sharpnose Shark ellipse. Isotopic values of all Atlantic Sharpnose Sharks combined were nonnormally distributed, but data for August-caught age-0 individuals were normally distributed.

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Varying degrees of isotopic niche overlap among the predators were detected with SEAs, as is expected with a relatively limited isotopic range. However, many species within the community had a large amount of unique isotopic niche space—that is, isotopic space within an ellipse that did not overlap with another species' ellipse. We attributed this result to differences in δ15N values, suggesting resource partitioning. Red Drum, Finetooth Sharks, and Cownose Rays had large areas of unique isotopic niche space, indicating possible instances of resource partitioning. Red Drum relied more heavily on a saltmarsh cordgrass-based food web (defined by more enriched δ13C values; Peterson and Fry 1987; Wright et al. 2014), and a large proportion of the diet was likely contributed by Atlantic marsh fiddler crabs (Wenner et al. 1990). Finetooth Sharks and Blacktip Sharks had the tightest clustering of isotopic signatures (M-NND and SD-NND) and the smallest CD values, which suggested a specialized diet (Layman et al. 2007). Both of those shark species are considered to be piscivorous feeders (Bethea et al. 2004), and this was confirmed by their stable isotope values. Finetooth Sharks and Blacktip Sharks had very similar δ15N ranges, but their δ13C values were distinct, suggesting dietary resource partitioning between the two species. Finetooth Sharks and Cownose Rays relied almost strictly on the planktonic food web (specified by the less-enriched δ13C values; France 1995). Cownose Rays are known to feed heavily on filter-feeding bivalve mollusks (Smith and Merriner 1985; Ajemian and Powers 2011); therefore, the Cownose Ray provided a good reference for the planktonic food web. The diets of Finetooth Sharks within Gulf of Mexico estuaries included a very high proportion of planktivorous Atlantic Menhaden (June and Carlson 1971; Hoffmayer and Parsons 2003; Bethea et al. 2004), and this finding is supported by our SIA results. Evidence of resource partitioning was found during two other community stable isotopic studies: one study was focused on elasmobranch mesopredators (Vaudo and Heithaus 2011), and the other study included predatory elasmobranchs and teleosts (Kinney et al. 2011). Thus, such partitioning may be common when relatively large predator communities co-exist within a habitat.

The small range of average δ15N values (3.3‰) for predatory fishes indicated that the predator community of Bulls Bay fell within a single trophic level, similar to the results of other community studies in which coexisting species showed high degrees of overlap in stable isotope values (Kinney et al. 2011; Vaudo and Heithaus 2011). However, if we consider all of the sampled individuals, Bulls Bay has a higher trophic diversity with less overlap in δ15N values relative to those previous studies. The only groups without significantly different mean values of δ15N from each other included Sandbar Sharks, Atlantic Sharpnose Sharks, and Spotted Seatrout. These three species have similar feeding habits and consume a mixed diet that includes invertebrate and teleost prey (Wenner et al. 1990; Gelsleichter et al. 1999; Bethea et al. 2004; Ellis and Musick 2007), which would place them at similar trophic positions.

Although the mean δ15N values of all predators fell within the δ15N range of a single trophic level, the species did not necessarily occupy the same trophic level of the food web. The isotopic signatures of Cownose Rays could be used as a trophic position reference due to their strict diet of bivalve mollusks (Collins et al. 2007; Ajemian and Powers 2011), which occupy a basal trophic level as planktonic filter feeders (Moncreiff and Sullivan 2001). The teleosts and other elasmo-branch species do not consume a significant amount of mollusks and therefore occupy higher positions within the estuarine food web. Red Drum and Bonnetheads were a slight step up in the trophic food web because they consume large proportions of crustaceans—mostly Atlantic marsh fiddler crabs (Red Drum) and blue crabs (Bonnetheads; Wenner et al. 1990; Cortes et al. 1996; Bethea et al. 2011)—which have slightly more enriched δ15N values than bivalves (Sullivan and Moncreiff 1990). Blue crabs and Atlantic marsh fiddler crabs were sampled for SIA, and their isotopic signatures fell about one trophic level below those of the Bonnetheads and Red Drum, respectively. Slightly higher above Red Drum and Bonnetheads in isotopic niche space were the predators that consume mixed diets of crustaceans and teleosts: such predators included Southern Flounder, Spotted Seatrout (Wenner et al. 1990), Atlantic Sharpnose Sharks (Hoffmayer and Parsons 2003), and Sandbar Sharks (Ellis and Musick 2007). Higher still were the predominantly piscivorous fish, such as Finetooth Sharks and Blacktip Sharks (Bethea et al. 2004; Barry et al. 2008), which displayed the most enriched δ15N values (Davenport and Bax 2002).

FIGURE 3.

Dietary niche overlap for 10 predator species in Bulls Bay, South Carolina. The lines enclose areas that represent each species' corrected standard ellipse area, which was calculated with the program SIBER. The overlap between ellipses represents the degree of shared resource use by two species. Although all sampled individuals are represented on the isotopic biplot, the ellipse for Atlantic Sharpnose Sharks was calculated based on only the August-caught age-0 fish and adults, as those data were normally distributed. The ellipse for Cownose Rays was calculated with nonnormally distributed data, whereas data from the other eight species had normal distributions.

f03_46.jpg

The teleosts as a group had a diverse diet, as suggested by their fairly high CD values and high variances in δ13C values (Layman et al. 2007); this observation is in agreement with the results of SCA (Wenner et al. 1990). Spotted Seatrout and Southern Flounder appeared to consume a variety of prey species with similar trophic positions, as indicated by their relatively small δ15N range; in contrast, the larger δ15N range for Red Drum suggested that they fed on prey across different trophic levels, such as Atlantic marsh fiddler crabs, shrimp, and teleosts (Wenner et al. 1990). Overall, the intermediate to high variances in δ13C and CD values for Scalloped Hammerheads, Sandbar Sharks, and Atlantic Sharpnose Sharks suggested that these species have also adopted a generalist feeding strategy in Bulls Bay, similar to the results of previous SCAs (Hoffmayer and Parsons 2003; Ellis and Musick 2007; Bethea et al. 2011).

Individuals or species with intermediate δ13C values may have mixed diets across food webs (feeding on pelagic and benthic prey) or may specialize on prey from the benthic food web (Fry and Sherr 1984). The majority of the predator community (excluding Red Drum, Finetooth Sharks, and Cownose Rays) shared similar basal resources, as indicated by two largely overlapping groups detected by Tukey's HSD test, which separated the species based on their δ13C values. Spotted Seatrout, Southern Flounder, Sandbar Sharks, Blacktip Sharks, Atlantic Sharpnose Sharks, Bonnetheads, and Scalloped Hammerheads had similar isotopic values that were associated with estuarine sediment values. These seven species could be exploiting a sediment-based food web or could be feeding on a diversity of prey spanning multiple food webs, which appears as a benthic-oriented diet in isotopic niche space (Bearhop et al. 2004).

The small δ13C range across the community implies that the predators were probably not feeding from the full potential breadth of basal resources within the area. It is possible either that (1) limited carbon sources support the predator community (Layman et al. 2007) or (2) we did not sample all of the carbon sources within Bulls Bay. Since estuaries are mixed bodies of water and since terrestrial and marine carbon resources mix with those found in the estuary (Leakey et al. 2008), gathering the distinct carbon signatures of different food web bases is difficult, and a single isotopic sample may contain multiple basal resources (e.g., a phytoplankton sample may contain saltmarsh cordgrass detritus as well). Because of the difficulty in distinguishing specific carbon resources (i.e., extricating microphytobenthos from the benthic samples or separating saltmarsh cordgrass from the suspended particulates), the sampled sources most likely did not represent the full breadth of food sources for lower-trophic-level organisms within the Bulls Bay estuary. Therefore, our results regarding the importance of particular carbon sources in predator diets are only indicative.

To incorporate age-0 elasmobranchs in an isotopic community study, we found it necessary to examine issues associated with interpreting the isotopic ratios of young sharks. There are three main factors that can confound SIA and the interpretation of isotopic values for age-0 and juvenile individuals of species (particularly elasmobranchs) that live only part of their life in the estuary. These interrelated factors include (1) variation in isotopic signals between estuarine and coastal or offshore waters, which can affect the interpretation of isotopic signatures from species that leave the estuary over the winter (Hobson 1999; Leakey et al. 2008; Shiffman et al. 2014); (2) tissue turnover, which is species specific and can affect the interpretation of isotopic signatures for age-0 individuals and migrating species (Logan and Lutcavage 2010; Olin et al. 2011); (3) inherent isotopic variation; and (4) maternal influence on the isotopic signatures of age-0 fish, which may affect the interpretation of those signatures (Olin et al. 2011).

Estuarine isotopic ratios exhibit depleted δ13C and enriched δ15N values in comparison with marine isotopic ratios (McClelland et al. 1997; Leakey et al. 2008); this can be of use to infer whether the tissues of predators truly reflect their estuarine diet. In the present study, teleost isotopic signatures reflected an estuarine isotopic baseline, as the fish remained in the estuary year-round (Wenner et al. 1990). The isotopic signatures for the elasmobranch species, which leave the estuary for coastal and offshore waters when water temperatures decline, showed similar or more depleted δ13C values than the signatures for teleosts. This finding suggests either that (1) coastal/offshore influences on isotopic signatures due to migration are minimal or (2) muscle tissue turnover in neonates and juveniles is fairly quick.

Although the range of estuarine isotopic ratios is distinct from those of marine and freshwater ratios (Peterson and Fry 1987), the isotopic structure of estuaries can be complex due to mixed diets and opportunistic feeding by predators and due to multiple organic matter sources (Peterson et al. 1985, 1994). In addition, estuaries have changing values of dissolved inorganic carbon (Peterson 1999) and physical mixing, such as bioturbation, which causes sediments at various depth levels to become mixed and to be suspended in the water column and therefore mixed with pelagic carbon (Ember et al. 1987). Due to these complexities, mixing models for the present study were considered unsuccessful in estimating the importance of carbon sources or potential prey items for the predators in Bulls Bay.

Tissue turnover rate and isotopic discrimination factors are species specific and can vary for a number of reasons, including diet type and diet isotope ratios, growth rate, and metabolic processes (McCutchan et al. 2003; Caut et al. 2009). It is preferable to use species-specific values for studies, although the extensive data required for all species are lacking. The discrimination values used for the current study were selected from the study by McCutchan et al. (2003), who provided potentially average discriminated factors across δ13C and δ15N for the 10 predator species. The tissue turnover rate in juvenile Sandbar Sharks was shown to begin reflecting a new diet (i.e., diverging from the maternal signature) with a half-life of about 80–100 d for δ15N and about 130–180 d for δ13C (Logan and Lutcavage 2010). Malpica-Cruz et al. (2012) studied neonate and age-0 Leopard Sharks and reported that muscle tissue turnover took approximately 192 d to reach equilibrium with the diet. Leopard Sharks have low metabolic rates, and their metabolic turnover contributed considerably to isotopic turnover (Malpica-Cruz et al. 2012). Therefore, muscle turnover in age-0 fish adapted to fast swimming with higher metabolic rates could be faster due to a more rapid growth rate in addition to metabolic tissue replacement (Trueman et al. 2005; Carleton and Martinez del Rio 2010; Weidel et al. 2011). The most rapid growth period for sharks occurs at the age-0 and early juvenile stages, when sharks use estuaries as nurseries (Branstetter 1987; Hoenig and Gruber 1990). The teleost species and some of the shark species (e.g., Atlantic Sharpnose Shark) examined in the current study have a rapid early growth rate (Wenner et al. 1990; Loefer and Sedberry 2003) and therefore are likely to exhibit a higher tissue turnover rate than adult sharks. However, if the tissue turnover rates of the age-0 sharks in this study were comparable to those of adult sharks, then their isotopic signatures should be interpreted with caution and may not fully reflect their current diet (Belicka et al. 2012).

It is important to be mindful of inherent isotopic variation within a population when interpreting results that focus on differences in variation, such as dietary niche overlap. A repeated analysis of four subsamples per individual showed that muscle samples from the same individual varied by an average of 0.11‰ for δ13C and by an average of 0.19‰ for δ15N (averages derived from three species: the Scalloped Hammerhead, Finetooth Shark, and Atlantic Sharpnose Shark).

Although the predators were analyzed as a single group across years to maintain sample size robustness, the interannual difference in δ13C may affect interpretation of the proportion of carbon sources that are important to a predator's diet. The shift in δ13C values across the sampling years may be attributable not to changes in predator diets or prey diets but rather to environmental changes in carbon composition. A shift in the predominance—and therefore the importance—of carbon sources in the estuary and/or the amount of carbon mixing involved could have been caused by a large difference in water temperature: January temperature averaged 6.5°C in 2011 and 11.5°C in 2012 (USGS 2013). Rainfall could have also slightly affected carbon sources; the estimated precipitation during the year preceding the 2011 sampling period was much higher than the estimate for the year preceding the 2012 sampling period (Northeast Regional Climate Center 2015). With slightly less rain in 2012, perhaps the estuary received a larger marine influence (with less freshwater runoff) during that year and therefore had more enriched δ13C values, since the estuarine environment is depleted in 13C relative to the marine environment (McClelland et al. 1997).

Our results indicated that even though the overall predatory fish community of Bulls Bay has relatively low trophic diversity, there is high variability in species-specific trophic diversity. Some species displayed large or completely unique isotopic niche spaces, indicating potential resource partitioning. Other species demonstrated dietary niche overlap as well as some unique niche space, which showed that they shared prey types with certain community members but also consumed distinctive prey items. Varying degrees of isotopic niche overlap among the predators with a relatively limited isotopic range across the community indicated possible functional redundancy (i.e., distinct species exhibiting similar functional roles [in terms of diet] in an ecosystem). Ecosystems with a higher functional redundancy are more stable and more resilient to changes in community structure, such as a reduced population of a given species (Lundberg and Moberg 2003). Additional research on stomach contents integrated with SIA of these predators would (1) help to clarify the trophic relationships among the predator species and between the predators and their prey and (2) allow exploration of individual dietary specialization within species.

Combining trophic information across multiple species inhabiting an area, their movements within that area, and environmental factors provides a comprehensive view of the ecosystem and a better understanding of how the animals are trophically and spatially connected to each other as well as to the environment. Consumption of prey from different food webs within an ecosystem allows predators to exploit different energy channels, possibly leading to a stabilizing effect on the community and ecosystem (Arim et al. 2010; Rooney and McCann 2012). Fishing pressure or natural disruptions of community structure may upset the stability of the estuarine food web. Our data contribute to baseline information about the trophic relationships within an ecologically and economically important estuarine community; such information is necessary to detect any future changes in trophic structure. Our results demonstrate that due to the interconnectedness of the species within a community, obtaining an understanding of a single species' trophic ecology within an estuary is not necessarily the best strategy to support the management of that species. It is vital to study not only the species of interest but also how those species interact with other members of their community, their prey, and their environment. This highlights the importance of gathering important communitywide trophic information, particularly when designing an ecosystem-based management plan.

ACKNOWLEDGMENTS

We thank Erin Levesque, Henry DaVega, Jonathan Tucker, and Michelle Taliercio (South Carolina Department of Natural Resources [SCDNR], Inshore Fisheries) for field assistance; Bill Roumillat for his local expertise and overall intellectual contribution to the study; and Andrew Grosse (SCDNR) for creating the location map. We also thank the organic chemistry metrology group within the National Institute of Standards and Technology; Kevin Beauchesne and Lisa May (Hollings Marine Laboratory) for laboratory support; and Jill Olin and Allan Strand for statistical analysis guidance. We are particularly grateful to Jay Brandes (SKIO-SSIL) for his instruction on SIA and data interpretation. This work was supported by the College of Charleston through internal grants (Faculty Research Grant and Biology Department Research and Development Grant) awarded to G. Sancho and by the SCDNR's Cooperative Atlantic States Shark Pupping and Nursery (COASTSPAN) survey sanctioned by the National Marine Fisheries Service. Fish handling and muscle biopsy were conducted in accordance with Permit 2012-002 from the Institutional Animal Care and Use Committee at the College of Charleston. This is Contribution 743 of the South Carolina Marine Resources Center.

REFERENCES

1.

Ajemian, M. J. , and S. P. Powers . 2011. Habitat-specific feeding by Cownose Rays (Rhinoptera bonasus) of the northern Gulf of Mexico. Environmental Biology of Fishes 95:79–97. Google Scholar

2.

Akin, S. , and K. O. Winemiller . 2008. Body size and trophic position in a temperate estuarine food web. Acta Oecologica 33:144–153. Google Scholar

3.

Arim, M. , S. R. Abades , G. Laufer , M. Loureiro , and P. A. Marquet . 2010. Food web structure and body size: trophic position and resource acquisition. Oikos 119:147–153. Google Scholar

4.

Baker, R. F. , P. J. Blanchfield , M. J. Paterson , R. J. Flett , and L. Wesson . 2004. Evaluation of nonlethal methods for the analysis of mercury in fish tissue. Transactions of the American Fisheries Society 133:568–576. Google Scholar

5.

Barry, K. P. , R. E. Condrey , W. B. Driggers , and C. M. Jones . 2008. Feeding ecology and growth of neonate and juvenile Blacktip Sharks Carcharhinus limbatus in the Timbalier-Terrebone Bay complex, Louisiana, USA. Journal of Fish Biology 73:650–662. Google Scholar

6.

Baustian, M. M. , G. J. Hansen , A. de Kluijver , K. Robinson , E. N. Henry , L. B. Knoll , and C. C. Carey . 2014. Linking the bottom to the top in aquatic ecosystems: mechanisms and stressors of benthic-pelagic coupling. Pages 25–47 in P. Kemp , editor. Eco-DAS X symposium proceedings. Association for the Sciences of Limnology and Oceanography, Waco, Texas. Google Scholar

7.

Bearhop, S. , C. E. Adams , S. Waldron , R. A. Fuller , and H. MacLeod . 2004. Determining tropic niche width: a novel approach using stable isotope analysis. Journal of Animal Ecology 73:1007–1012. Google Scholar

8.

Belicka, L. L. , P. Matich , R. Jaffe , and M. R. Heithaus . 2012. Fatty acids and stable isotopes as indicators of early life feeding and potential maternal resource dependency in the Bull Shark Carcharhinus leucas. Marine Ecology Progress Series 455:245–256. Google Scholar

9.

Bethea, D. M. , J. A. Buckel , and J. K. Carlson . 2004. Foraging ecology of the early life stages of four sympatric shark species. Marine Ecology Progress Series 268:245–264. Google Scholar

10.

Bethea, D. M. , J. K. Carlson , L. D. Hollensead , Y. P. Papastamatiou , and B. S. Graham . 2011. A comparison of the foraging ecology and bioenergetics of the early life stages of two sympatric hammerhead sharks. Bulletin of Marine Science 87:873–889. Google Scholar

11.

Blaber, S. J. M. , D. P. Cyrus , J.-J. Albaret , C. V. Ching , J. W. Day , M. Elliott , M. S. Fonseca , D. E. Hoss , J. Orensanz , I. C. Potter , and W. Silvert . 2000. Effects of fishing on the structure and functioning of estuarine and nearshore ecosystems. ICES Journal of Marine Science 57:590–602. Google Scholar

12.

Bligh, E. G. , and W. J. Dyer . 1959. A rapid method of total lipid extraction and purification. Canadian Journal of Biochemistry and Physiology 37:911–917. Google Scholar

13.

Borrell, A. , L. Cardona , R. P. Kumarran , and A. Aguilar . 2011. Trophic ecology of elasmobranchs caught off Gujarat, India, as inferred from stable isotopes. ICES Journal of Marine Science 68:547–554. Google Scholar

14.

Branstetter, S. 1987. Age and growth validation of newborn sharks held in laboratory aquaria, with comments on the life history of the Atlantic Sharpnose Shark, Rhizoprionodon terraenovae. Copeia 1987:291–300. Google Scholar

15.

Bush, A. 2003. Diet and diel feeding periodicity of juvenile Scalloped Hammerhead Sharks, Sphyrna lewini, in Kane'ohe Bay, O'ahu, Hawai’i. Environmental Biology of Fishes 67:1–11. Google Scholar

16.

Carleton, S. A. , and C. Martinez del Rio . 2010. Growth and catabolism in isotopic incorporation: a new formulation and experimental data. Functional Ecology 24:805–812. Google Scholar

17.

Castro, J. I. 1993. The shark nursery of Bulls Bay, South Carolina, with a review of the shark nurseries of the southeastern coast of the United States. Environmental Biology of Fishes 38:37–48. Google Scholar

18.

Caut, S. , E. Angulo , and F. Courchamp . 2009. Variation in discrimination factors (Δ15N and Δ13C): the effect of diet isotopic values and applications for diet reconstruction. Journal of Applied Ecology 46.2:443–453. Google Scholar

19.

Christie, W. W. 1993. Preparation of lipid extracts from tissues. Pages 195–213 in W. W. Christie , editor. Advances of lipid methodology, volume 2. Oily Press, Dundee, UK. Google Scholar

20.

Collins, A. B. , M. R. Heupel , R. E. Hueter , and P. J. Motta . 2007. Hard prey specialists or opportunistic generalists? An examination of the diet of the Cownose Ray, Rhinoptera bonasus. Marine and Freshwater Research 58:135–144. Google Scholar

21.

Cortes, E. 1997. A critical review of methods of studying fish feeding based on analysis of stomach contents: application to elasmobranch fishes. Canadian Journal of Fisheries and Aquatic Sciences 54:726–738. Google Scholar

22.

Cortes, E. 1999. Standardized diet compositions and trophic levels of sharks. ICES Journal of Marine Science 56:707–717. Google Scholar

23.

Cortes, E. , C. A. Manire , and R. E. Hueter . 1996. Diet, feeding habits, and diel feeding chronology of the Bonnethead Shark, Sphyrna tiburo, in southwest Florida. Bulletin of Marine Science 58:353–367. Google Scholar

24.

Davenport, S. , and N. Bax . 2002. A trophic study of a marine ecosystem off southeastern Australia using stable isotopes of carbon and nitrogen. Canadian Journal of Fisheries and Aquatic Sciences 59:514–530. Google Scholar

25.

DeNiro, M. J. , and S. Epstein . 1981. Influence of diet on the distribution of nitrogen isotopes in animals. Geochimica et Cosmochimica Acta 45:341–351. Google Scholar

26.

Ellis, J. K. , and J. A. Musick . 2007. Ontogenetic changes in the diet of the Sandbar Shark, Carcharhinus plumbeus, in lower Chesapeake Bay and Virginia (USA) coastal waters. Environmental Biology of Fishes 80:51–67. Google Scholar

27.

Ember, L. M. , D. F. Williams , and J. T. Morris . 1987. Processes that influence carbon isotope variations in salt marsh sediments. Marine Ecology Progress Series 36:33–42. Google Scholar

28.

Ferry, L. A. , and G. M. Cailliet . 1996. Sample size and data analysis: are we characterizing and comparing diet properly? Pages 71–80 in D. MacKinlay and K. Shearer , editors. Feeding ecology and nutrition in fish. American Fisheries Society, Physiology Section, Bethesda, Maryland. Google Scholar

29.

France, R. L. 1995. Stable isotopic survey of the role of macrophytes in the carbon flow of aquatic foodwebs. Vegetatio 124:67–72. Google Scholar

30.

Fry, B. , and E. B. Sherr . 1984. δ13C Measurements as indicators of carbon flow in marine and freshwater ecosystems. Contributions in Marine Science 27:13–47. Google Scholar

31.

Gannes, L. Z. , D. M. O'Brien , and C. Martinez del Rio . 1997. Stable isotopes in animal ecology: assumptions, caveats, and a call for more laboratory experiments. Ecology 78:1271–1276. Google Scholar

32.

Gelsleichter, J. , J. A. Musick , and S. Nichols . 1999. Food habits of the Smooth Dogfish, Mustelus canis, Dusky Shark, Carcharhinus obscurus, Atlantic Sharpnose Shark, Rhizoprionodon terraenovae, and the Sand Tiger, Carcharias taurus, from the northwest Atlantic Ocean. Environmental Biology of Fishes 54:205–217. Google Scholar

33.

Herzka, S. Z. 2005. Assessing connectivity of estuarine fishes based on stable isotope ratio analysis. Estuarine, Coastal, and Shelf Science 64:58–69. Google Scholar

34.

Hobson, K. A. 1999. Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120:314–326. Google Scholar

35.

Hoenig, J. M. , and S. H. Gruber . 1990. Life-history patterns in the elasmobranchs: implications for fisheries management. NOAA Technical Report 90:1–16. Google Scholar

36.

Hoffmayer, E. R. , and G. R. Parsons . 2003. Food habits of three shark species from the Mississippi Sound in the northern Gulf of Mexico. Southeastern Naturalist 2:271–280. Google Scholar

37.

Hussey, N. E. , J. Brush , I. D. McCarthy , and A. T. Fisk . 2010. δ15N and δ13C diet-tissue discrimination factors for large sharks under semi-controlled conditions. Comparative Biochemistry and Physiology A 155:445–453. Google Scholar

38.

Hussey, N. E. , M. A. MacNeil , J. A. Olin , B. C. McMeans , M. J. Kinney , D. D. Chapman , and A. T. Fisk . 2012. Stable isotopes and elasmobranchs: tissue types, methods, applications and assumptions. Journal of Fish Biology 80(Special Issue): 1449–1484. Google Scholar

39.

Hyslop, E. J. 1980. Stomach contents analysis—a review of methods and their application. Journal of Fish Biology 17:411–429. Google Scholar

40.

Jackson, A. L. , R. Inger , A. C. Parnell , and S. Bearhop . 2011. Comparing isotopic niche widths among and within communities: SIBER-stable isotope Bayesian ellipses in R. Journal of Animal Ecology 180:595–602. Google Scholar

41.

June, F. C. , and F. T. Carlson . 1971. Food of young Atlantic Menhaden, Brevoortia tyrannus, in relation to metamorphosis. U.S. National Marine Fisheries Service Fishery Bulletin 68:493–512. Google Scholar

42.

Kennish, M. J. 2002. Environmental threats and environmental future of estuaries. Environmental Conservation 29:78–107. Google Scholar

43.

Kim, S. L. , D. R. Casper , F. Galvan-Magana , R. Ochoa-Diaz , S. B. Hernandez-Aguilar , and P. L. Koch . 2012. Carbon and nitrogen discrimination factors for elasmobranch soft tissues based on a long term controlled feeding study. Environmental Biology of Fishes 95:37–52. Google Scholar

44.

Kim, S. L. , and P. L. Koch . 2012. Methods to collect, preserve, and prepare elasmobranch tissues for stable isotope analysis. Environmental Biology of Fishes 95:53–63. Google Scholar

45.

Kinney, M. J. , N. E. Hussey , A. T. Fisk , A. J. Tobin , and C. A. Simpfendorfer . 2011. Communal or competitive? Stable isotope analysis provides evidence of resource partitioning within a communal shark nursery. Marine Ecology Progress Series 439:263–276. Google Scholar

46.

Latour, R. J. , M. J. Brush , and C. F. Bonzek . 2003. Toward ecosystem-based fisheries management. Fisheries 28(9):10–22. Google Scholar

47.

Layman, C. A. , D. A. Arrington , C. G. Montana , and D. M. Post . 2007. Can stable isotope ratios provide for community-wide measures of trophic structure? Ecology 88:42–48. Google Scholar

48.

Leakey, C. D. B. , M. J. Attrill , S. Jennings , and M. F. Fitzsimons . 2008. Stable isotopes in juvenile marine fishes and their invertebrate prey from the Thames Estuary, UK, and adjacent coastal regions. Estuarine, Coastal, and Shelf Science 77:513–522. Google Scholar

49.

Lellis-Dibble, K. A. , K. E. McGlynn , and T. E. Bigford . 2008. Estuarine fish and shellfish species in U.S. commercial and recreational fisheries: economic value as an incentive to protect and restore estuarine habitat. NOAA Technical Memorandum NMFS-F/SPO-90. Google Scholar

50.

Levin, P. S. , M. J. Fogarty , S. A. Murawski , and D. Fluharty . 2009. Integrated ecosystem assessments: developing the scientific basis for ecosystem-based management of the ocean. PLoS (Public Library of Science) Biology [online serial] 7(1):e1000014. Google Scholar

51.

Link, J. S. , and H. I. Browman . 2014. Integrating what? Levels of marine ecosystem-based assessment and management. ICES Journal of Marine Science 71:1170–1173. Google Scholar

52.

Loefer, J. K. , and G. R. Sedberry . 2003. Life history of the Atlantic Sharpnose Shark (Rhizoprionodon terraenovae) (Richardson, 1836) off the southeastern United States. U.S. National Marine Fisheries Service Fishery Bulletin 101:75–88. Google Scholar

53.

Logan, J. M. , and M. E. Lutcavage . 2010. Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644:231–244. Google Scholar

54.

Lundberg, J. , and F. Moberg . 2003. Mobile link organisms and ecosystem functioning: implications for ecosystem resilience and management. Ecosystems 6:87–98. Google Scholar

55.

MacAvoy, S. E. , S. A. Macko , and G. C. Garman . 2001. Isotopic turnover in aquatic predators: quantifying the exploitation of migratory prey. Canadian Journal of Fisheries and Aquatic Sciences 58:923–932. Google Scholar

56.

MacNeil, M. A. , K. G. Drouillard , and A. T. Fisk . 2006. Variable uptake and elimination of stable nitrogen isotopes between tissues in fish. Canadian Journal of Fisheries and Aquatic Sciences 63:345–353. Google Scholar

57.

Malpica-Cruz, L. , S. Z. Herzka , O. Sosa-Nishizaki , and J. P. Lazo . 2012. Tissue-specific isotope trophic discrimination factors and turnover rates in a marine elasmobranch: empirical and modeling results. Canadian Journal of Fisheries and Aquatic Sciences 69:551–564. Google Scholar

58.

Mathews, T. D. , F. W. Stapor Jr. , C. R. Richter , J. V. Miglarese , M. D. McKenzie , and L. A. Barclay . 1980. Ecological characterization of the Sea Island coastal region of South Carolina and Georgia, volume 1: physical features of the characterization area. U.S. Fish and Wildlife Service FWS/OBS-79/40. Google Scholar

59.

McClelland, J. W. , I. Valiela , and R. H. Michener . 1997. Nitrogen stable isotope signatures in estuarine food webs: a record of increasing urbanization in coastal watersheds. Limnology and Oceanography 42:930–937. Google Scholar

60.

McCutchan, J. H. , W. M. Lewis , C. Kendall , and C. C. McGrath . 2003. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102:378–390. Google Scholar

61.

McMeans, B. C. , J. A. Olin , and G. W. Benz . 2009. Stable-isotope comparisons between embryos and mothers of a placentatrophic shark species. Journal of Fish Biology 75:2464–2474. Google Scholar

62.

Minagawa, M. , and E. Wada . 1984. Stepwise enrichment of 15N along food chains: further evidence and the relationship between δ15N and animal age. Geochimica et Cosmochimica Acta 48:1135–1140. Google Scholar

63.

Moncreiff, C. A. , and M. J. Sullivan . 2001. Trophic importance of epiphytic algae in subtropical seagrass beds: evidence from multiple stable isotope analyses. Marine Ecology Progress Series 215:93–106. Google Scholar

64.

Newsome, S. D. , C. Martinez del Rio , S. Bearhop , and D. L. Phillips . 2007. A niche for isotopic ecology. Frontiers in Ecology and the Environment 5:429–436. Google Scholar

65.

Northeast Regional Climate Center. 2015. Climate information for management and operational decisions (CLIMOD 2). Cornell University, Ithaca, New York. Available:  climodtest.nrcc.cornell.edu. (October 2015). Google Scholar

66.

Olin, J. A. , N. E. Hussey , M. Fritts , M. R. Heupel , C. A. Simpfendorfer , G. R. Poulakis , and A. T. Fisk . 2011. Maternal meddling in neonatal sharks: implications for interpreting stable isotopes in young animals. Rapid Communications in Mass Spectrometry 25:1008–1016. Google Scholar

67.

Papastamatiou, Y. P. , B. M. Wetherbee , C. G. Lowe , and G. L. Crow . 2006. Distribution and diet of four species of carcharhinid shark in the Hawaiian Islands: evidence for resource partitioning and competitive exclusion. Marine Ecology Progress Series 320:239–251. Google Scholar

68.

Parnell, A. C. , and A. L. Jackson . 2013. Stable isotope analysis in R (SIAR) package. Available:  cran.r-project.org/web/packages/siar/index.html. (September 2014). Google Scholar

69.

Peterson, B. , B. Fry , M. Hullar , and S. Saupe . 1994. The distribution and stable carbon isotopic composition of dissolved organic carbon in estuaries. Estuaries 17:111–121. Google Scholar

70.

Peterson, B. J. 1999. Stable isotopes as tracers of organic matter input and transfer in benthic food webs: a review. Acta Oecologica 20:479–487. Google Scholar

71.

Peterson, B. J. , and B. Fry . 1987. Stable isotopes in ecosystem studies. Annual Review of Ecology and Systematics 18:293–320. Google Scholar

72.

Peterson, B. J. , R. W. Howarth , and R. H. Garritt . 1985. Multiple stable isotopes used to trace the flow of organic matter in estuarine food webs. Science 227:1361–1363. Google Scholar

73.

Phillips, D. L. 2012. Converting isotope values to diet composition: the use of mixing models. Journal of Mammalogy 93:342–352. Google Scholar

74.

Pinnegar, J. K. , and N. V. C. Polunin . 1999. Differential fractionation of δ13C and δ15N among fish tissues: implications for the study of trophic interactions. Functional Ecology 13:225–231. Google Scholar

75.

Pinnegar, J. K. , N. V. C. Polunin , P. Francour , F. Badalamenti , R. Chemello , M.-L. Harmelin-Vivien , B. Hereu , M. Milazzo , M. Zabala , G. D'Anna , and C. Pipitone . 2000. Trophic cascades in benthic marine ecosystems: lessons for fisheries and protected-area management. Environmental Conservation 27:179–200. Google Scholar

76.

Pikitch, E. , C. Santora , E. A. Babcock , A. Bakun , R. Bonfil , D. O. Conover , P. Dayton , P. Doukakis , D. Fluharty , B. Heneman , E. D. Houde , J. Link , P. A. Livingston , M. Mangel , M. K. McAllister , J. Pope , and K. J. Sainsbury . 2004. Policy forum: ecosystem-based fisheries management. Science 305:346–347. Google Scholar

77.

R Development Core Team. 2010. R: a language and environment for statistical computing, version 2.8.1. R Foundation for Statistical Computing, Vienna. Available:  www.R-project.org. (September 2014). Google Scholar

78.

Rooney, N. , and K. S. McCann . 2012. Integrating food web diversity, structure and stability. Trends in Ecology and Evolution 27:40–46. Google Scholar

79.

Sandifer, P. , J. Miglarese , D. Calder , J. Manzi , and L. Barclay . 1980. Ecological characterization of the sea island coastal region of South Carolina and Georgia, volume III: biological features of the characterization area. U.S. Fish and Wildlife Service FWS/OBS-79/42. Google Scholar

80.

Shiffman, D. S. , B. S. Frazier , J. R. Kucklick , D. Abel , J. Brandes , and G. Sancho . 2014. Feeding ecology of the Sandbar Shark in South Carolina estuaries revealed through δ13C and δ15N stable isotope analysis. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science [online serial] 6:156–169. Google Scholar

81.

Shiffman, D. S. , A. J. Gallagher , M. D. Boyle , C. M. Hammerschlag-Peyer , and N. Hammerschlag . 2012. Stable isotope analysis as a tool for elasmobranch conservation research: a primer for non-specialists. Marine and Freshwater Research 63:635–643. Google Scholar

82.

Smith, J. W. , and J. V. Merriner . 1985. Food habits and feeding behavior of the Cownose Ray, Rhinoptera bonasus, in lower Chesapeake Bay. Estuaries 8:305–310. Google Scholar

83.

Sullivan, M. J. , and C. A. Moncreiff . 1990. Edaphic algae are an important component of salt marsh foodwebs: evidence from multiple stable isotope analyses. Marine Ecology Progress Series 62:149–159. Google Scholar

84.

Suzuki, K. W. , A. Kasai , K. Nakayama , and M. Tanaka . 2005. Differential isotopic enrichment and half-life among tissues in the Japanese temperate bass (Lateolabrax japonicus) juveniles: implications for analyzing migration. Canadian Journal of Fisheries and Aquatic Sciences 62:671–678. Google Scholar

85.

Syvaranta, J. , A. Lensu , T. J. Marjomaki , S. Oksanen , and R. I. Jones . 2013. An empirical evaluation of the utility of convex hull and standard ellipse areas for assessing population niche widths from stable isotope data. PLoS (Public Library of Science) ONE [online serial] 6:e56094. Google Scholar

86.

Torres-Rojas, Y. E. , A. Hernández-Herrera , F. Galván-Magaña , and V. G. Alatorre-Ramírez . 2010. Stomach content analysis of juvenile Scalloped Hammerhead Shark Sphyrna lewini captured off the coast of Mazatlán, Mexico. Aquatic Ecology 44:301–308. Google Scholar

87.

Trueman, C. N. , R. A. R. McGill , and P. H. Guyard . 2005. The effect of growth rate on diet-tissue isotopic spacing in rapidly growing animals: an experimental study with Atlantic Salmon (Salmo salar). Rapid Communications in Mass Spectrometry 19:3239–3247. Google Scholar

88.

Ulrich, G. F. , C. M. Jones , W. B. Driggers , J. M. Drymon , D. Oakley , and C. Riley . 2007. Habitat utilization, relative abundance, and seasonality of sharks in the estuarine and nearshore waters of South Carolina. Pages 125–139 in C. T. McCandless , N. E. Kohler , and H. L. Pratt Jr., editors. Shark nursery grounds of the Gulf of Mexico and the East Coast waters of the United States. American Fisheries Society, Symposium 50, Bethesda, Maryland. Google Scholar

89.

USGS (U.S. Geological Survey). 2013. Current conditions for the nation. Available:  http://nwis.waterdata.usgs.gov/nwis/uv?. (June 2013). Google Scholar

90.

Vaudo, J. J. , and M. R. Heithaus . 2011. Dietary niche overlap in a nearshore elasmobranch mesopredator community. Marine Ecology Progress Series 425:247–260. Google Scholar

91.

Vaudo, J. J. , P. Matich , and M. R. Heithaus . 2010. Mother-offspring isotope fractionation in two species of placentatrophic sharks. Journal of Fish Biology 77:1724–1727. Google Scholar

92.

Weidel, B. C. , S. R. Carpenter , J. F. Kitchell , and M. J. Vander Zanden . 2011. Rates and components of carbon turnover in fish muscle: insights from bioenergetics models and a whole-lake 13C addition. Canadian Journal of Fisheries and Aquatic Sciences 68:387–399. Google Scholar

93.

Wenner, C. A. , W. A. Roumillat , J. E. Moran , M. B. Maddox , L. B. Daniel , and J. W. Smith . 1990. Investigations on the life history and population dynamics of marine recreational fishes in South Carolina, part 1. South Carolina Department of Natural Resources, Special Publication, Charleston. Google Scholar

94.

Wright, J. T. , J. E. Byers , J. L. DeVore , and E. E. Sotka . 2014. Engineering or food? Mechanisms of facilitation by a habitat-forming invasive seaweed. Ecology 95:2699–2706. Google Scholar

Appendices

Appendix: Summary of Additional Stable Isotope Information

TABLE A. 1.

Mean (±SD) δ13C and δ15N values for potential prey taxa and basal carbon resources used by the predator community in Bulls Bay, South Carolina.

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© Ashley L. Shaw, Bryan S. Frazier, John R. Kucklick, Gorka Sancho This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ashley L. Shaw, Bryan S. Frazier, John R. Kucklick, and Gorka Sancho "Trophic Ecology of a Predatory Community in a Shallow-Water, High-Salinity Estuary Assessed by Stable Isotope Analysis," Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 8(8), 46-61, (16 March 2016). https://doi.org/10.1080/19425120.2015.1121940
Received: 31 August 2015; Accepted: 9 November 2015; Published: 16 March 2016
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